Operational Data Integration: A new Frontier for Data Management

The amount and diversity of work done by data integration specialists has exploded since the turn of the twenty-first century. Analytic data integration continues to be a vibrant and growing practice that’s applied most often to data warehousing and business intelligence initiatives. But a lot of the growth comes from the emerging practice of operational data integration, which is usually applied to the migration, consolidation, or synchronization of operational databases, plus business-to-business data exchange. Analytic and operational data integration are both growing; yet, the latter is growing faster in some sectors.

But growth comes at a cost. Many corporations have staffed operational data integration by borrowing data integration specialists from data warehouse teams, which puts important BI work in peril. Others have gone to the other extreme by building new teams and infrastructure that are redundant with analytic efforts. In many firms, operational data integration’s contributions to the business are limited by legacy, hand-coded solutions that are in dire need of upgrade or replacement. And the best practices of operational data integration on an enterprise scale are still coalescing, so confusion abounds.

The purpose of this report is to identify the best practices and common pitfalls involved in starting and sustaining a program for operational data integration. The report defines operational data integration in terms of its relationship to other data integration practices, as well as by its most common project types. Along the way, we’ll look at staffing and other organizational issues, followed by a list of technical requirements and vendor products that apply to operational data integration projects.